This course is aimed at students in year 4 with a specialization that includes Statistics.
Pre-requisites: (a) Stat 306 or comparable course with multiple and
binary regression, (b) Stat 302 or introductory probability,
(c) basic familiarity with R for plots and regression,
familiarity with adding libraries in R.
In the academic calendar, Stat 305 is the pre-requisite for a generic Stat 447 Topics in Statistics course. For this specific topics course, Stat 306 is the most important pre-requisite, and Stat 305 is helpful but not necessary. If you have taken Stat 406, then you have more options for the team project.
Topics: Statistical computing topics covered include: R statistical software, reproducible code, pseudo-code, writing functions, documentation, validation, debugging, numerical methods, profiling. A team project is the major activity.
The goal of this course is for you to enjoy doing a project making use of theory and methods learned in previous statistics courses. Code used for the project should be written in a modular, reproducible style; and functions should be used for algorithms and repetitive steps.
Course learning objectives. The emphasis is on development of skills: report writing, presentation, code writing, data sense, data analysis, statistical inference. These skills will help in post-graduation job hunting in data science and other career paths.
Course material will be relevant to the data sets and projects that the class is working on. For a project, possibilities are something from www.kaggle.com
1. Data Wrangling with R, by B C Boehmke, Springer. Available electronically at www.library.ubc.ca
2. Reproducible Code, published by British Ecological Society. BES-Guide-Reproducible-Code-2019.pdf